323 citations
- Claremont Graduate UniversityUS16 papers
- Scripps CollegeUS10 papers
- Pomona CollegeUS9 papers
- Harvey Mudd CollegeUS7 papers
- Pitzer CollegeUS7 papers
- Georgia Institute of TechnologyUS4 papers
- Arizona State UniversityUS3 papers
- Jagiellonian UniversityPL3 papers
- Massachusetts Institute of TechnologyUS3 papers
- University of California, Los AngelesUS3 papers
- University of ConnecticutUS3 papers
- University of South FloridaUS3 papers
6 papers · 2 filters
Practical approximate projection schemes in greedy signal space methods
Chris Garnatz, Xiaoyi Gu, Alison Kingman +3
Compressive sensing (CS) is a new signal acquisition paradigm which shows that far fewer samples are required to reconstruct sparse signals than previously thought. Although most o…
Linear Convergence of Stochastic Iterative Greedy Algorithms with Sparse Constraints
Nam Nguyen, Deanna Needell, Tina Woolf
Motivated by recent work on stochastic gradient descent methods, we develop two stochastic variants of greedy algorithms for possibly non-convex optimization problems with sparsity…
Block Kaczmarz Method with Inequalities
Jonathan Briskman, Deanna Needell
The randomized Kaczmarz method is an iterative algorithm that solves overdetermined systems of linear equations. Recently, the method was extended to systems of equalities and ineq…
A Comparison of Clustering and Missing Data Methods for Health Sciences
Ran Zhao, Deanna Needell, Christopher Johansen +1
In this paper, we compare and analyze clustering methods with missing data in health behavior research. In particular, we propose and analyze the use of compressive sensing's matri…
Randomized Block Kaczmarz Method with Projection for Solving Least Squares
Deanna Needell, Ran Zhao, Anastasios Zouzias
The Kaczmarz method is an iterative method for solving overcomplete linear systems of equations Ax=b. The randomized version of the Kaczmarz method put forth by Strohmer and Vershy…
Near Oracle Performance and Block Analysis of Signal Space Greedy Methods
Raja Giryes, Deanna Needell
Compressive sampling (CoSa) is a new methodology which demonstrates that sparse signals can be recovered from a small number of linear measurements. Greedy algorithms like CoSaMP h…